885 citations · 1.7k across the 71 of their papers we have counts for
9 papers · 1 filter
Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic
Rodolfo Valiente, Behrad Toghi, Mahdi Razzaghpour +2
It is expected that autonomous vehicles(AVs) and heterogeneous human-driven vehicles(HVs) will coexist on the same road. The safety and reliability of AVs will depend on their soci…
Equal Improvability: A New Fairness Notion Considering the Long-term Impact
Ozgur Guldogan, Yuchen Zeng, Jy-yong Sohn +2
Devising a fair classifier that does not discriminate against different groups is an important problem in machine learning. Although researchers have proposed various ways of defin…
An Optimal Transport Approach to Personalized Federated Learning
Farzan Farnia, Amirhossein Reisizadeh, Ramtin Pedarsani +1
Federated learning is a distributed machine learning paradigm, which aims to train a model using the local data of many distributed clients. A key challenge in federated learning i…
Straggler-Resilient Personalized Federated Learning
Isidoros Tziotis, Zebang Shen, Ramtin Pedarsani +2
Federated Learning is an emerging learning paradigm that allows training models from samples distributed across a large network of clients while respecting privacy and communicatio…
Binary Classification Under Attacks for General Noise Distribution
Payam Delgosha, Hamed Hassani, Ramtin Pedarsani
Adversarial examples have recently drawn considerable attention in the field of machine learning due to the fact that small perturbations in the data can result in major performanc…
Provably Private Distributed Averaging Consensus: An Information-Theoretic Approach
Mohammad Fereydounian, Aryan Mokhtari, Ramtin Pedarsani +1
In this work, we focus on solving a decentralized consensus problem in a private manner. Specifically, we consider a setting in which a group of nodes, connected through a network,…
Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic
Rodolfo Valiente, Behrad Toghi, Ramtin Pedarsani +1
Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in the real world where they will be surrounded by human-driven vehicles (HVs) is extremely ch…
Imitation Learning by Estimating Expertise of Demonstrators
Mark Beliaev, Andy Shih, Stefano Ermon +2
Many existing imitation learning datasets are collected from multiple demonstrators, each with different expertise at different parts of the environment. Yet, standard imitation le…
Efficient and Robust Classification for Sparse Attacks
Mark Beliaev, Payam Delgosha, Hamed Hassani +1
In the past two decades we have seen the popularity of neural networks increase in conjunction with their classification accuracy. Parallel to this, we have also witnessed how frag…